Difference between revisions of "Orange: Interpolate"
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* Multi-variate interpolation interpolates the whole series table as a two-dimensional plane instead of as separate single-dimensional series. | * Multi-variate interpolation interpolates the whole series table as a two-dimensional plane instead of as separate single-dimensional series. | ||
| − | Pada Widget Interpolate, nilai yang hilang pada nilai akhir series (awal dan ekor) selalu diinterpolasi menggunakan metode terdekat. Kecuali jika metode interpolasi diatur ke nearest, discrete time series (seperti sequences) selalu dikaitkan dengan mode | + | Pada Widget Interpolate, nilai yang hilang pada nilai akhir series (awal dan ekor) selalu diinterpolasi menggunakan metode terdekat. Kecuali jika metode interpolasi diatur ke nearest, discrete time series (seperti sequences) selalu dikaitkan dengan mode series (most frequent value). |
==Contoh== | ==Contoh== | ||
Revision as of 11:37, 9 March 2020
Sumber: https://orange.biolab.si/widget-catalog/time-series/interpolate/
Mengira-ngira nilai yang hilang dalam time series dengan interpolasi.
Input
Time series: Time series as output by As Timeseries widget.
Output
Time series: The input time series with the chosen default interpolation method for when the algorithms require interpolated time series (without missing values). Interpolated time series: The input time series with any missing values interpolated according to the chosen interpolation method.
Sebagian besar algoritma Time Series mengasumsikan, kita tidak memiliki nilai yang hilang dalam data kita. Dalam widget ini, kita dapat memilih metode interpolasi untuk mengira-ngira nilai yang hilang. Secara default, widget Interpolate akan menggunakan interpolasi linier (cepat dan masuk akal).
- Interpolation type. You can select one of linear, cubic spline, nearest, or mean interpolation:
- Linear interpolation replaces missing values with linearly-spaced values between the two nearest defined data points.
- Spline interpolation fits a cubic polynomial to the points around the missing values. This is a painfully slow method that usually gives best results.
- Nearest interpolation replaces missing values with the previous defined value.
- Mean interpolation replaces missing values with the series’ mean.
- Multi-variate interpolation interpolates the whole series table as a two-dimensional plane instead of as separate single-dimensional series.
Pada Widget Interpolate, nilai yang hilang pada nilai akhir series (awal dan ekor) selalu diinterpolasi menggunakan metode terdekat. Kecuali jika metode interpolasi diatur ke nearest, discrete time series (seperti sequences) selalu dikaitkan dengan mode series (most frequent value).
Contoh
Masukan sebuah time series dengan nilai yang hilang, memperoleh keluaran time series yang di interpolasi.
